On Comparison of Classical and Machine Learning Models for Forecasting Rabi Green Gram Production in Odisha
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Authors
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Subhalaxmi Biswas
Odisha University of Agriculture and Technology, Bhubaneswar
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Abhiram Dash
Odisha University of Agriculture and Technology, Bhubaneswar
Keywords:
: Akaike Information Criteria Corrected; Forecasting; Hybrid ARIMA-ANN; linearity; Non- linearity.Abstract
Green gram is one of the major pulse crop of Odisha grown mostly during the Rabi seasons. Rabi green gram production accounts for approximately
51 percent of the total pulse production in Odisha. Data on production (in ‘000 MT) of rabi green gram are collected for the period from 1970-71 to
2023-2024. The entire data is splitted into two sets: training data set and testing/Cross validated data set. The data for the period from 1970-71 to 2019
20 for building training data set and 2020-21 to 2023-24 to be used as test data set/ cross validation data set. Classical Auto Regressive Integrated
Moving Average (ARIMA) model and Artificial Neural Network (ANN) model is built for the training set data. Both the built-in models are compared
on basis of Mean Absolute Percentage Error MAPE and Akaike Information Criteria Corrected (AICc) for the training set and test set data. The model
performing better on basis of MAPE and AICc is selected for obtaining final forecast values of rabi green gram production.
ARIMA (2, 0, 0) fits best which after performing the BDS test for the residuals shows that both linearity and non-linearity is found in the data in ratio
of 7:1. So hybrid ARIMA-ANN model is prescribed to be fitted with appropriate weightage to selected best fit ARIMA and ANN models. Out of the
suitable NNAR models, NNAR (2, 3) model to be the best fit. So the hybrid ARIMA-ANN model is fitted with weightage of 0.875 to the selected
best fit ARIMA (2,0,0) model and 0.125 to the selected best fit NNAR (2,3) model. The model fit statistics for the hybrid ARIMA-ANN model is
found to be lower in case of training data set and testing data set. The forecast values of rabi green gram production obtained from the fitted hybrid
ARIMA-ANN model are found to be decreasing in future years. Thus, it could be concluded that the advanced machine learning model definitely
improves the accuracy of forecasting.
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References
Dash, A, Dhakre, DS and Bhattacharya D.(2017). Fitting of appropriate
model to study growth rate and instability of mango production in
India. Agric. Sci. Digest., 37(3) 2017: 191-196
Dash, A.Mangaraju, A, Mishra, P. and Nayak H. (2020). Using
Autoregressive Integrated Moving Average(ARIMA) Technique
to Forecast the Production of Kharif Cereals in Odisha (India).
Current Journal of Applied Science and Technology, 39(9):
104-113
Dash, A.Dhakre, D.S. and Bhattacharya D. (2017). Study of Growth
and Instability in Food Grain Productionof Odisha: A Statistical
Modelling Approach, Environment & Ecology, 35(4D) : 3341-3351
Dash, A, Dhakre, D.S. and Bhattacharya, D. (2017). Analysis of
Growth and Instability in Pulse Production of Odisha during Rabi
Season: A Statistical Modelling Approach, International Journal
of Current Microbiology and Applied Science, 6(8): 107-115
TABLES AND FIGURES
363
Dash, A. and Hansdah, R. (2020). Analytical study of growth and
instability of rabi oilseed production in Odisha. Journal of
Pharmacognosy and Phytochemistry 2020, 9(2), 2145-2149
Devi M, Kumar J, Malik D P and Mishra, P.(2021). Forecasting of
wheat production in Haryana using hybrid time series model.
Journal of Agriculture and Food Research, 5, 100175.
Dibya, B and Dash, A. (2022). Using ARIMA Model to Forecast the
Area, Yield and Production of Arhar in Odisha. Biological Forum.
14(3), 1179-1185
Garai, S., Paul, R.K., Yeasin, M. and Paul, A.K. (2024). CEEMDAN
Based Hybrid Machine Learning Models for Time Series
Forecasting Using MARS Algorithm and PSO-Optimization.
Neural Processing Letters, https://doi.org/10.1007/s11063-024
11552-w
Mishra, P. Khatib, AMGA, Lal P, Anwar, A.N., Korakot, Abotaleb,
M, Ray, S and Veerasak, P. (2023). An Overview of Pulses
Production in India: Retrospect and Prospects of the Future Food
with an Application of Hybrid Models. National Academy Science
Letters. 46, 366-374
Mitra, D. and Paul, R.K. (2017). Hybrid time-series models for
forecasting agricultural commodity prices. Model Assisted
Statistics and Applications, 12, 255-264.
Paul, R.K., Das, T. and Yeasin, M. (2023). Ensemble of Time Series and
Machine Learning Model for Forecasting Volatility in Agricultural
Prices. National Science Academy Science Letter.https://doi.
org/10.1007/s40009-023-01218-x
Pradhan, J and Dash, A. (2024). Using ARIMA Model to Forecast
Production of kharif Sweet Potato in Odisha.Environment and
Ecology,. 42(2):383-390
Raghav Y S, Mishra P, Alakkari K M, Singh M, Al Khatib A M G and
Balloo, R (2022). Modelling and forecasting of pulses production
in south Asian countries and its role in nutritional security. Legume
Research-An International Journal, 45(4), 454-461.
Rakshit, D., & Paul, R.K. (2024). Hybrid ARFIMA-LRNN Model for
Forecasting Commodity Prices. Journal of the Indian Society of
Agricultural Statistics, 78(3): 221-229
Sahu, G and Dash, A. (2022). Forecasting of Area, Yield and Production
of Mustard in Odisha. Biological Forum. 14(3): 1157-1163
Sameerbanu, P. (2024). Forecasting Onion Production in India Using
Arima Models: A Research Study. International Journal of
Scientific Research in Engineering and Management. 8(8): 1-7
Sankar, T.J. and Pushpa, P.(2025). Design and Development of ARIMA
Model for Allium Cepa Production in India. Palestine Journal of
Mathematics, 14.
Yeasin, M., Paul, R.K. & Shankar, S.V. (2024). Ensemble machine
learning models for forecasting tropical cyclones in North Indian
region. Earth Sci Inform. https://doi.org/10.1007/s12145-024
01366-8
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